System for identifying high risk parking lots

ABSTRACT

Systems and methods are disclosed for identifying high risk parking lots. High risk parking lots may be, for example, parking lots that pose a higher than average risk of collisions and/or theft. Auto insurance claim data may be analyzed to identify hazardous areas. A virtual navigation of roads within the hazardous area may be identified. Public parking lots within the virtual navigation map may be defined, with each public parking lot determined as either in a hazardous area or not. A vehicle may be determined to be approaching or parking in a parking lot in a hazardous area, and a nearby public parking lot not associated with the hazardous area may be selected instead. A route from a current position to the nearby public parking lot may be generated, and the vehicle may be routed to the nearby public parking lot. As a result, collisions and thefts may be reduced.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/482,506, filed Apr. 7, 2017 and entitled “System for Identifying HighRisk Parking Lots.”

This application claims priority to and the benefit of the filing dateof (1) provisional U.S. Application Ser. No. 62/321,005, filed Apr. 11,2016 and entitled “Device for Detecting and Visualizing High RiskIntersections and Other Areas,” (2) provisional U.S. Application Ser.No. 62/321,010, filed Apr. 11, 2016 and entitled “ANALYZING AUTO CLAIMAND VEHICLE COLLISION DATA TO IDENTIFY HAZARDOUS AREAS AND REDUCEVEHICLE COLLISIONS,” and (3) provisional U.S. Application Ser. No.62/340,302, filed May 23, 2016 and entitled “Analyzing Auto Claim andVehicle Collision Data to Identify Hazardous Areas and Reduce VehicleCollisions,” the entire disclosures of which are incorporated herein byreference.

TECHNICAL FIELD

This disclosure relates to a device for detecting and visualizinghigh-risk parking lots and other areas for vehicle drivers.

BACKGROUND

Drivers and passengers assume a certain degree of risk of injury orproperty damage when travelling by vehicle. This risk may be mitigatedby reducing or eliminating certain contributing factors. For example, adriver may avoid risky behavior, such as driving while intoxicated,driving while tired, or driving while texting. As another example, adriver may mitigate the risk of serious injury by driving a car withsafety features such as airbags, seatbelts, and antilock brakes.

However, certain risk factors may not be mitigated. For example, thevery nature of a vehicle may present certain inherent risks. A typicalcar may weigh thousands of pounds and may not always maneuver or stopquickly. When travelling at even a moderate speed, a collision mayresult in serious damage to the vehicle and serious injury to theoccupants. Further, a driver may have no control over perhaps thegreatest risk factor involved with driving: other drivers. Furthermore,a vehicle, particularly an unattended vehicle, may at any time be atrisk of theft and/or vandalism.

These risks are particularly present in the case of parking lots. Inaddition to the collision risks faced by a vehicle entering or existinga parking lot, a vehicle within a parking lot may continue to be at riskof collision as a result of insufficient turning radii along circulatingroads and parking aisles, constrained or minimally-sized parking spacesavailable for each vehicle, that result in close proximity to othervehicles, and visual obstruction caused by structural columns andfeatures, landscaping or other parked vehicles. Further, a vehiclewithin a parking lot, by its very nature, is known to likely beunattended, placing the vehicle at a potentially increased risk of theftand/or vandalism.

Vehicle collisions and theft may result in significant damages to, oreven total losses of, vehicles. Vehicle collision and theft may requireextensive resources to rectify, such as monies and time. The vehicledamage may negatively impact those involved, and may be time consumingand lead to annoyance or inconvenience. Vehicle collisions and theft mayalso suffer from other drawbacks, such as requiring public resources tofacilitate a response, such as police and medical personal. The presentembodiments may overcome these and/or other deficiencies.

SUMMARY

The present embodiments disclose systems and methods that may relate to,inter alia, vehicle collisions, and/or identifying hazardous areas, inparticular parking lots, that are associated with an above averageamount of vehicle collisions and/or theft. Systems and methods may usehistorical auto insurance claim data and/or other data (such as vehiclecollision data, mobile device data, telematics data, vehiclemounted-sensor or image data, autonomous vehicle sensor or image data,and/or smart infrastructure sensor or image data) to determine hazardousareas that are prone to induce, or be associated with, vehiclecollisions.

In one embodiment, a computer-implemented method may be provided. Themethod may include (1) analyzing, via one or more processors, autoinsurance claim data to identify hazardous areas, the hazardous areabeing defined, at least in part, by GPS location or GPS coordinates; (2)building or generating, via the one or more processors, a virtualnavigation map of roads within the hazardous area, the virtualnavigation map being visually depicted; (3) identifying, via the one ormore processors, public parking lots within the virtual navigation map,and determining whether each public parking lot is associated with ahazardous area or not; (4) determining, via the one or more processors,that a vehicle is approaching or parking in a public parking lotassociated with a hazardous area; (5) selecting, via the one or moreprocessors, a nearby public parking lot that is not associated with ahazardous area; (6) generating, via the one or more processors, a routefrom the current position to the nearby public parking lot that is notassociated with a hazardous area; and/or (7) routing, via one or moretransceivers and/or the one or more processors, the vehicle to thenearby public parking lot using the generated route. Additional, fewer,or modified elements of the method may be possible.

In another embodiment, a computer system may be provided. The system maycomprise (1) one or more processors; and (2) one or more memoriesstoring computer-executable instructions that, when executed, cause theprocessor to: (i) analyze auto insurance claim data to identifyhazardous areas, the hazardous areas being defined, at least in part, byGPS location or GPS coordinates, (ii) build or generate a virtualnavigation map of roads within the hazardous area, the virtualnavigation map being visually depicted, (iii) identify public parkinglots within the virtual navigation map, and determine whether eachparking lot is associated with a hazardous area or not; (iv) determinethat a vehicle is approaching or parking in a public parking lotassociated with a hazardous area; (v) select a nearby public parking lotthat is not associated with a hazardous area; (vi) generate a route froma current position to the nearby public parking lot that is notassociated with a hazardous area; and/or (vii) route or reroute thevehicle to the nearby public parking lot using the generated route.Additional, fewer, or modified system components may be possible.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1A illustrates a block diagram of an exemplary interconnectedwireless communication network that is configured to collect customerdata (which may include telematics data, mobile device data,vehicle-mounted sensor data; auto insurance claim data, autonomousvehicle data, etc.);

FIG. 1B illustrates a block diagram of another exemplary interconnectedwireless communication network that is configured to collect customerdata, such as customer data generated or collected by mobile devices;smart or interconnect home controllers; autonomous or smart vehiclemonitoring systems; sensor and/or image data, and/or smartinfrastructure or traffic lights;

FIG. 2 illustrates a block diagram of an exemplary on-board computer ormobile device;

FIGS. 3A & 3B illustrate an exemplary computer-implemented method ofusing auto insurance claim data, vehicle collision data, and/or othercustomer data to identify hazardous areas and re-route autonomousvehicles to reduce future vehicle collisions;

FIG. 4 illustrates an exemplary computer-implemented method ofidentifying hazardous areas and determining whether to engage ordisengage autonomous features prior to an autonomous vehicle passingthrough the hazardous area;

FIG. 5 illustrates an exemplary computer-implemented method of buildinghazardous area virtual maps and generating alerts for bicyclist orpedestrian wearable electronics; and

FIG. 6 illustrates an exemplary computer-implemented method ofidentifying high risk parking lots and routing vehicles to alternateparking lots associated with lower risk of collision or theft.

DETAILED DESCRIPTION

The present embodiments may relate to, inter alia, identifying hazardousareas, in particular parking lots, using auto insurance claim, vehiclecollision data, and/or other data. Virtual navigation maps depicting thehazardous areas may be generated and used to re-route autonomous orother vehicles, bicyclists, and/or pedestrians to avoid the hazardousareas. Also, alerts may be generated to notify drivers, bicyclists,and/or pedestrians of an approaching hazardous area and/or type ofhazards presented by the area. Further, based upon the type of hazardspresented, autonomous features may be recommended to be engaged ordisengaged if the vehicle is going to travel through the hazardous area.

Identifying Hazardous Areas Using Auto Claim & Vehicle Collision Data

The present embodiments relate to identifying risks associated withhazardous areas or intersections, portions of roads, or other areas,including high risk intersections and road segments. Vehicle collisiondata may be gathered from various sources, such as from processors,transceivers, sensors, and/or cameras associated with smartinfrastructure, smart or autonomous vehicles, mobile devices, and/orvarious sensors. The vehicle collision data may also be associated withactual insurance claims arising from real world vehicle collisions, suchas data scrubbed of personal information, or otherwise de-identifiedauto insurance claim data. In one embodiment, actual claim images (suchas mobile device images of damaged vehicles, or images acquired viavehicle-mounted cameras and/or sensors) may be analyzed to associate anamount of physical damage shown in one or more images of a vehicleinvolved in a vehicle collision with a repair or replacement cost of thevehicle. The actual claim images may be used to estimate repair orreplacement cost for vehicles involved in past, more recent, or currentvehicle collisions.

Further, past or current vehicle collision data may be analyzed todetermine a cause of each vehicle collision, such as identify one ormore vehicles or vehicle drivers at fault, faulty street signs ortraffic lights, missing stop or other traffic signs, road construction,blind spots, irregular or unusual traffic flows (causing driverconfusion or autonomous vehicle confusion), weather conditions, trafficconditions, etc.

The vehicle collision data may be used to generate an electronic alertor indication to a driver or an autonomous vehicle. The alert mayindicate an approaching hazardous area that has been identified by thevehicle collision data or auto insurance claim data. Once notified of ahazardous area, an autonomous vehicle may reroute itself around thehazardous area, such as take an alternative route to avoid a high riskroad segment or confusing traffic pattern. Additionally oralternatively, an autonomous vehicle may engage or disengage certainautonomous features when approaching, and/or traveling through, ahazardous area. For instance, hazardous areas may be classified, such asincluding an exit ramp, on-ramp, circular traffic pattern, intersection,road construction or daily changing traffic flow, abnormal traffic flow,narrowing number of lanes (such as 5 lanes becoming 4 or even 3 lanesleading to traffic backups), the temporary occurrence of inclementweather that contributes to suboptimal road surface conditions, trafficmerging from the left or in another abnormal manner, etc.

The present embodiments may collect vehicle collision data associatedwith vehicles involved in vehicle collisions while being manuallydriven, as well as being autonomously driven. The vehicle collision datamay be analyzed to determine whether certain intersections or roadsegments, or other hazardous areas, are safer when vehicles travelingthrough them are manually or autonomously driven, or are safer whenspecific autonomous features or systems or engaged or disengaged forspecific types of hazardous areas.

The vehicle collision data may also include indications or images of theenvironment of the hazardous area before, during, and/or after thevehicle collision. For instance, the vehicle collision data may beanalyzed to determine traffic light operation and status before avehicle collision. The vehicle collision data may be analyzed todetermine whether pedestrian walk lights and/or smart infrastructure areleading to, or causing, vehicle collisions. As an example, a trafficlight may change to rapidly for traffic conditions, or may not allowenough time for pedestrians to cross the road. Two or more trafficlights at a given intersection may not be synchronized correctly.

To facilitate diverting vehicle, bicycle, and/or pedestrian traffic fromhazardous areas of road, such as high risk intersections or roadsegments, a toll may be charged for using the hazardous area of road.Vehicle transponders, or wearable electronics on bicyclists orjoggers/walkers, may communicate with smart infrastructure and deposit agiven amount of money from a virtual account of the user each time theuser uses, or travels through, the hazardous area of road.

The vehicle collision data (including the auto insurance claim data) maybe used to determine when (e.g., a time of day) and/or under whatenvironment conditions that the hazardous area is at a lower or higherrisk of vehicle collision. Certain road areas may be more problematicduring rush hour or at night, or when under construction, or when it israining or snowing. The vehicle collision data may be used to identify atime of year that the hazardous area is at a lower or elevated risk,such as certain bridges or ramps may be at a higher risk when theweather includes freezing rain or ice during winter months.

A virtual map of the hazardous areas may be downloaded and displayed foruser review. A heads up display of each hazardous area may be displayedon a dashboard of user's vehicle when the user's vehicle is within apredetermined distance of the hazardous area, such as one mile, andtraveling along a route to the hazardous area. The virtual map mayalternatively be displayed on an in-board navigation unit of the user'svehicle, or via a mobile device or wearable electronics device display.The virtual map may be superimposed on a windshield, such as on thepassenger's side of the windshield, in other embodiments.

The present embodiments may provide a remote server that (i) collectsvehicle collision data via wireless communication or data transmissionover one or more radio links or wireless communication channels; (ii)determines hazardous areas from the vehicle collision data and/or autoclaim data (which may include past or current vehicle collisionlocation, time, day, extent of damage, and/or cause information); and(iii) generates a virtual navigation map of the hazardous areas. Afterwhich, the remote server may transmit the hazardous area information andrecommendations to vehicles, mobile devices, or wearable electronics ofa user via wireless communication or data transmission over one or moreradio links or wireless communication channels. With the user'spermission, whether or not the travel recommendations are followed bythe user or by the user's autonomous vehicle may be tracked, and theresults or frequency of the user or the user's vehicle following therecommendations to reduce risk of vehicle collision may be transmittedto the remote server. After which, the remote server may update oradjust an auto, personal, health, or other insurance premium or discountto reflect risk averse behavior of the user.

The recommendations may include that an autonomous or semi-autonomousvehicle engage one or more autonomous or semi-autonomous features orsystems at a predetermined distance prior to reaching the hazardousarea, such as one or more miles. It may be recommended that vehicleequipped with fully autonomous functionality engage that functionalityprior to entering the hazardous area if the functionality has proven tobe less risky or safer that manual driving or manual vehicle operationduring the same type of hazardous area approaching.

For instance, traffic patterns that require a human driver to attend totwo or more things—such as speed (accelerating or braking), changinglanes (merging with traffic, leaving one flow of traffic for another,signaling lanes changes, determining when to exit or what road to take,weather, traffic lights, traffic signs, detours resulting from roadclosures, road maintenance or construction, or motor vehicle collision,etc.—may be confusing for a human driver driving in unfamiliar territoryor on new roads. In such instances, such as for circular trafficpatterns, intersections, exist ramps, on-ramps, etc., an autonomousvehicle may perform better than average humans, or with less risk orchance of vehicle collision.

The present embodiments may determine a score for each hazardous areaidentified, and if a score is greater than a predetermined threshold,then a vehicle with autonomous or self-driving functionality mayautomatically, or at the direction of a human driver or passenger,engage one or more autonomous features or systems at a predetermineddistance from the hazardous area. The autonomous feature(s) may thendisengage at a predetermined distance from the hazardous area oncemanually-controlled driving has resumed, or after the driver hasindicated that she is ready to reassume direct control of the vehicle.

The present embodiments may also use the vehicle collision data todetermine low risk or safer routes for bicyclists and/or pedestrians tofollow that avoid higher risk areas. As an example, risk avoidanceroutes may be developed for school children to follow before and afterschool, whether on foot or bike. Bicyclists may be routed in citytraffic along lower risk routes, such as in the direction that is alongwith one-way traffic flow, and/or along routes with fewer intersectionsor bike paths or bridges.

The present embodiments may include smart infrastructure, such as smarttraffic lights, equipped with various sensors, such as radar units thatmay detect vehicle speed. If an approaching vehicle is traveling over agiven amount of speed, the smart traffic light may change (green,yellow, or red) lights repeatedly, or flash the lights, to give thevehicle and/or driver an indication of an approaching intersectionand/or that the vehicle is traveling too fast and needs to slow down, oreven come to a halt at the intersection.

The hazardous areas may be characterized as to why they are high risk.For example, certain intersections or portions of roads may beassociated with a higher-than-average number of vehicle, bicycle, and/orpedestrian collisions, a higher amount of traffic, an extensive durationor scope of road construction or maintenance, abnormal traffic patterns,auto insurance claims including more serious vehicle damage orpedestrian and passenger damages, an increase in collision frequencyrelated to the occurrence of inclement weather that temporarily affectsroad surface quality (water, snow, ice) and visibility (fog, rain) etc.

Other hazardous areas may be associated with parking lots that have anabnormally high amount of vehicle collisions and/or vehicle theft. Highrisk parking lots may be identified and mapped. Vehicles, includingautonomous vehicles, may be notified of the high risk parking lots andalternate parking lots associated with lower risk, such as associatedwith fewer vehicle collisions or stolen vehicles, may be identified orselected. The vehicles may be routed or re-routed to the alternateparking lots associated with lower risk to facilitate safer vehicleoperation and reduce vehicle theft.

Exemplary Autonomous Vehicle Operation System

FIG. 1A illustrates a block diagram of an exemplary interconnectedwireless communication system 100 that is configured to collect customerdata, such as customer data generated or collected by mobile devices;smart infrastructure, various sensors and cameras, and/or autonomous orsmart vehicle monitoring systems—on which the exemplary methodsdescribed herein may be implemented. The customer data may detail, or beassociated with, vehicle collisions or near collisions.

The customer data may be generated and/or collected by mobiledevice-mounted sensors, vehicle-mounted sensors, and/or smartinfrastructure-mounted sensors. The sensors may include cameras andother sensors mentioned herein. The sensor data may be collected before,during, and/or after vehicle collisions. The high-level architecture mayinclude both hardware and software applications, as well as various datacommunications channels for communicating data between the varioushardware and software components.

The communication system 100 may be roughly divided into front-endcomponents 102 and back-end components 104. The front-end components 102may obtain information regarding a customer from a number of computingdevices, such as wearable electronics (e.g., an augmented realityappliance) or mobile devices 110, smart home controllers, smartbicycles, and vehicles 108 (e.g., a car, truck, motorcycle, etc.), smartinfrastructure, and the surrounding environment.

With respect to the vehicles 108, an on-board computer 114 may generateor collect various types of information from vehicle-mounted sensors.For instance, an autonomous vehicle may collect data related to theautonomous features to assist the vehicle operator in operating thevehicle 108. To monitor the vehicle 108, the front-end components 102may include one or more sensors 120 installed within the vehicle 108that may communicate with the on-board computer 114. The front-endcomponents 102 may further process the sensor data using the on-boardcomputer 114 or a mobile device 110 (e.g., a smart phone, a tabletcomputer, a special purpose computing device, smart watch, wearableelectronics such as an augmented reality appliance, etc.) to determinewhen the vehicle is in operation and information regarding the vehicle,such as GPS or other location data.

In some embodiments of the system 100, the front-end components 102 maycommunicate with the back-end components 104 via a network 130. Eitherthe on-board computer 114 or the mobile device 110 may communicate withthe back-end components 104 via the network 130 to allow the back-endcomponents 104 to record information regarding vehicle or mobile deviceusage, including vehicle or mobile device GPS data, video data, and/ortelematics data. The back-end components 104 may use one or more servers140 to receive data from the front-end components 102, store thereceived data, process the received data, and/or communicate informationassociated with the received or processed data.

The front-end components 102 may be disposed within or communicativelyconnected to one or more on-board computers 114, which may bepermanently or removably installed in the vehicle 108. The on-boardcomputer 114 may interface with the one or more sensors 120 within thevehicle 108 (e.g., a digital camera, a video camera, a LIDAR sensor, anultrasonic sensor, an infrared sensor, an ignition sensor, an odometer,a system clock, a speedometer, a tachometer, an accelerometer, agyroscope, a compass, a geolocation unit, radar unit, etc.), whichsensors may also be incorporated within or connected to the on-boardcomputer 114.

The front end components 102 may further include a communicationcomponent 122 to transmit information to and receive information fromexternal sources, including other vehicles, infrastructure, smart homecontrollers or sensors, or the back-end components 104. In someembodiments, the mobile device 110 may supplement the functionsperformed by the on-board computer 114 described herein by, for example,sending or receiving information to and from the mobile server 140 viathe network 130, such as over one or more radio frequency links orwireless communication channels. In other embodiments, the on-boardcomputer 114 may perform all of the functions of the mobile device 110described herein, in which case no mobile device 110 may be present inthe system 100.

Either or both of the mobile device 110 or on-board computer 114 (and/ora smart home controller, sensor, or processor) may communicate with thenetwork 130 over links 112 and 118, respectively. Either or both of themobile device 110 or on-board computer 114 (and/or a smart homecontroller, sensor, or processor) may run a Data Application forcollecting, generating, processing, analyzing, transmitting, receiving,and/or acting upon customer data associated with the vehicle 108 (e.g.,sensor data, route and/or destination data, GPS data) or the vehicleenvironment (e.g., other vehicles operating near the vehicle 108).Additionally, the mobile device 110 and on-board computer 114 (and/orsmart home controller or computer) may communicate with one anotherdirectly over link 116 or indirectly over multiple radio links, and/ormay be configured for vehicle navigation and/or virtual map display.

The mobile device 110 may be either a general-use personal computer,cellular phone, smart phone, tablet computer, smart watch, wearableelectronics, vehicle navigation device, or a dedicated vehiclemonitoring or control device. Although only one mobile device 110 isillustrated, it should be understood that a plurality of mobile devices110 may be used in some embodiments, such as mobile devices associatedwith a family or household. The on-board computer 114 may be ageneral-use on-board computer capable of performing many functionsrelating to vehicle operation or a dedicated computer for autonomousvehicle operation. Further, the on-board computer 114 may be originallyinstalled by the manufacturer of the vehicle 108, or installed as anaftermarket modification or addition to the vehicle 108. In someembodiments or under certain conditions, the mobile device 110 oron-board computer 114 (or smart home controller) may function asthin-client devices that outsource some or most of the processing to theserver 140.

The sensors 120 may be removably or fixedly installed within the vehicle108 and may be disposed in various arrangements to provide informationgeneration and collection of customer data, and/or to provideinformation to the autonomous operation features. Among the sensors 120may be included one or more of a GPS unit, a radar unit, a LIDAR unit,an ultrasonic sensor, an infrared sensor, an inductance sensor, acamera, an accelerometer, a tachometer, or a speedometer. Some of thesensors 120 (e.g., radar, LIDAR, or camera units) may actively orpassively scan the vehicle environment for obstacles (e.g., othervehicles, buildings, pedestrians, etc.), roadways, lane markings, signs,or signals. Other sensors 120 (e.g., GPS, accelerometer, or tachometerunits) may provide data for determining the location or movement of thevehicle 108. Other sensors 120 may be directed to the interior orpassenger compartment of the vehicle 108, such as cameras, microphones,pressure sensors, thermometers, or similar sensors to monitor thevehicle operator and/or passengers within the vehicle 108. Informationgenerated or received by the sensors 120 may be communicated to theon-board computer 114 or the mobile device 110 and/or smart homecontroller or smart infrastructure—such as collected and analyzed as thecustomer data discussed herein.

In further embodiments, the front-end components may include aninfrastructure communication device 124 for monitoring the status of oneor more infrastructure components 126. Infrastructure components 126 mayinclude roadways, bridges, traffic signals, gates, switches, crossings,parking lots or garages, toll booths, docks, hangars, or other similarphysical portions of a transportation system's infrastructure. Theinfrastructure communication device 124 may include or becommunicatively connected to one or more sensors (not shown) fordetecting information relating to the condition of the infrastructurecomponent 126. The sensors (not shown) may generate data relating toweather conditions, traffic conditions, or operating status of theinfrastructure component 126.

The infrastructure communication device 124 may be configured to receivethe sensor data generated and determine a condition of theinfrastructure component 126, such as weather conditions, roadintegrity, construction, traffic, available parking spaces, etc. Theinfrastructure communication device 124 may further be configured tocommunicate information to vehicles 108 via the communication component122. In some embodiments, the infrastructure communication device 124may receive information from one or more vehicles 108, while, in otherembodiments, the infrastructure communication device 124 may onlytransmit information to the vehicles 108. The infrastructurecommunication device 124 may be configured to monitor vehicles 108and/or directly or indirectly communicate information to other vehicles108 and/or to mobile devices 110.

In some embodiments, the communication component 122 may receiveinformation from external sources, such as other vehicles, mobiledevices, smart home controllers, or infrastructure. The communicationcomponent 122 may also send information regarding the vehicle 108 toexternal sources, such as mobile devices or smart home controllers. Tosend and receive information, the communication component 122 mayinclude a transmitter and a receiver designed to operate according topredetermined specifications, such as the dedicated short-rangecommunication (DSRC) channel, wireless telephony, Wi-Fi, or otherexisting or later-developed communications protocols. The receivedinformation may supplement the data received from the sensors 120. Forexample, the communication component 122 may receive information that anautonomous vehicle ahead of the vehicle 108 is reducing speed, allowingthe adjustments in an autonomous operation of the vehicle 108, if thevehicle is an autonomous or semi-autonomous vehicle.

In addition to receiving information from the sensors 120, the on-boardcomputer 114 may directly or indirectly control the operation of thevehicle 108 according to various autonomous operation features. Theautonomous operation features may include software applications ormodules implemented by the on-board computer 114 to generate andimplement control commands to control the steering, braking, or throttleof the vehicle 108. To facilitate such control, the on-board computer114 may be communicatively connected to control components of thevehicle 108 by various electrical or electromechanical controlcomponents (not shown). When a control command is generated by theon-board computer 114, it may thus be communicated to the controlcomponents of the vehicle 108 to effect a control action. In embodimentsinvolving fully autonomous vehicles, the vehicle 108 may be operableonly through such control components (not shown). In other embodiments,the control components may be disposed within or supplement othervehicle operator control components (not shown), such as steeringwheels, accelerator or brake pedals, or ignition switches.

In some embodiments, the front-end components 102 (such as mobiledevices, smart vehicles, smart homes, or other customer computers)communicate data (including the customer data discussed herein) with theback-end components 104 via the network 130. The network 130 may be aproprietary network, a secure public internet, a virtual private networkor some other type of network, such as dedicated access lines, plainordinary telephone lines, satellite links, cellular data networks,combinations of these. The network 130 may include one or more radiofrequency communication links, such as wireless communication links 112and 118 with mobile devices 110 and on-board computers 114 (and/or smarthome controllers), respectively. Where the network 130 comprises theInternet, data communications may take place over the network 130 via anInternet communication protocol.

The back-end components 104 may include one or more servers 140. Eachserver 140 may include one or more computer processors adapted andconfigured to execute various software applications and components. Theserver 140 may further include a database 146, which may be adapted tostore customer and/or other data received from mobile devices, vehicles,home computers, or smart infrastructure.

Such data might include, for example, data associated with vehiclecollisions or near vehicle collisions, vehicle damage, extent ofinjuries at a vehicle collision, vehicle collision data, number andidentification of vehicles involved, dates and times of vehicle use,duration of vehicle use, mobile device GPS location, vehicle GPSlocation, home occupancy or vacancy information, use and settings ofhome electronic features (such as operation of smart electronics withinthe home), and vehicle telematics data, such as speed of the vehicle108, RPM or other tachometer readings of the vehicle 108, lateral andlongitudinal acceleration of the vehicle 108, vehicle incidents or nearcollisions of the vehicle 108, hazardous or anomalous conditions withinthe vehicle operating environment (e.g., construction, accidents, etc.),communication between the autonomous operation features and externalsources, environmental conditions of vehicle operation (e.g., weather,traffic, road condition, etc.), errors or failures of smartinfrastructure or autonomous operation features, or other data relatingto use of the vehicle 108 and the autonomous operation features, whichmay be uploaded to the server 140 via the network 130.

The server 140 may access data stored in the database 146 whenidentifying high risk or hazardous areas (such as high riskintersections, roads, road segments, exit ramps, toll booths, or parkinglots), executing various functions and tasks associated with mapping thehazardous areas, generating alerts of approaching hazardous areas,and/or determining new routes for vehicles, pedestrians, or bicycliststhat avoid hazardous areas.

Although the system 100 is shown to include one vehicle 108, one mobiledevice 110, one on-board computer 114, and one server 140, it should beunderstood that different numbers of vehicles 108, mobile devices 110,on-board computers 114, and/or servers 140 may be utilized, includinginterconnected home or smart infrastructure (e.g., smart sign, roadmarker, bridge, road, or traffic light) processors and/or transceivers.For example, the system 100 may include a plurality of servers 140 andhundreds of mobile devices 110 or on-board computers 114, all of whichmay be interconnected via the network 130. Furthermore, the databasestorage or processing performed by the one or more servers 140 may bedistributed among a plurality of servers 140 in an arrangement known as“cloud computing.” This configuration may provide various advantages,such as enabling near real-time uploads and downloads of information, aswell as periodic uploads and downloads of information. This may in turnsupport a thin-client embodiment of the mobile device 110 or on-boardcomputer 114 discussed herein.

The server 140 may have a controller 155 that is operatively connectedto the database 146 via a link 156. It should be noted that, while notshown, additional databases may be linked to the controller 155 in aknown manner. For example, separate databases may be used for varioustypes of information, such as customer mobile device or vehicle locationinformation, tracking autonomous vehicle location, vehicle collisions,road conditions, road construction, vehicle insurance policy informationor vehicle use or maintenance information. Additional databases (notshown) may be communicatively connected to the server 140 via thenetwork 130, such as databases maintained by third parties (e.g.,weather, construction, or road network databases). The controller 155may include a program memory 160, a processor 162 (which may be called amicrocontroller or a microprocessor), a random-access memory (RAM) 164,and an input/output (I/O) circuit 166, all of which may beinterconnected via an address/data bus 165. It should be appreciatedthat although only one microprocessor 162 is shown, the controller 155may include multiple microprocessors 162. Similarly, the memory of thecontroller 155 may include multiple RAMs 164 and multiple programmemories 160. Although the I/O circuit 166 is shown as a single block,it should be appreciated that the I/O circuit 166 may include a numberof different types of I/O circuits. The RAM 164 and program memories 160may be implemented as semiconductor memories, magnetically readablememories, or optically readable memories, for example. The controller155 may also be operatively connected to the network 130 via a link 135.

The server 140 may further include a number of software applicationsstored in a program memory 160. The various software applications on theserver 140 may include an autonomous operation information monitoringapplication 141 for receiving information regarding the vehicle 108 andits autonomous operation features (which may include control commands ordecisions of the autonomous operation features), a feature evaluationapplication 142 for determining the effectiveness of autonomousoperation features under various conditions and/or determining operatingcondition of autonomous operation features or components, a risk mappingapplication 143 for determining and electronically mapping hazardousareas, such as areas that are more prone to be associated with vehiclecollisions than normal, and/or identifying the risks associated withautonomous operation feature use along a plurality of road segmentsassociated with an electronic map, a route determination application 144for determining routes that route a vehicle, pedestrian, or bicycle froma current GPS to a destination that avoids a hazardous area, or thatroute to an alternate or lower risk parking lot, and an autonomousparking application 145 for assisting in parking and retrieving anautonomous vehicle. The various software applications may be executed onthe same computer processor or on different computer processors.

FIG. 1B illustrates a block diagram of another wireless communicationsystem 180 on which the exemplary methods described herein may beimplemented. In one aspect, with customer permission or affirmativeconsent, system 180 may collect customer data, including the type ofcustomer data discussed herein (such as auto insurance claim data and/orvehicle collision data), and include a network 130, N number of vehicles182.1-182.N and respective mobile computing devices 184.1-184.N, anexternal computing device 186 (such as home computing device, or smarthome controller), and/or a smart infrastructure component 188. In oneaspect, mobile computing devices 184 may be an implementation of mobilecomputing device 110, while vehicles 182 may be an implementation ofvehicle 108, including autonomous or semi-autonomous vehicles. Thevehicles 182 may include a plurality of vehicles 108 having autonomousoperation features, as well as a plurality of other vehicles not havingautonomous operation features.

As illustrated, the vehicle 182.1 may include a vehicle controller181.1, which may be an on-board computer 114 as discussed elsewhereherein, while vehicle 182.2 may lack such a component. Each of vehicles182.1 and 182.2 may be configured for wireless inter-vehiclecommunication, such as vehicle-to-vehicle (V2V) or peer-to-peer wirelesscommunication and/or data transmission via the communication component122, directly via the mobile computing devices 184, or otherwise.

Although system 180 is shown in FIG. 1A as including one network 130,two mobile computing devices 184.1 and 184.2, two vehicles 182.1 and182.2, one external computing device 186, and/or one smartinfrastructure component 188, various embodiments of system 180 mayinclude any suitable number of networks 130, mobile computing devices184, vehicles 182, external (including home) computing devices 186,and/or infrastructure components 188. The vehicles 182 included in suchembodiments may include any number of vehicles 182.i having vehiclecontrollers 181.n (such as vehicle 182.1 with vehicle controller 181.1)and vehicles 182 j not having vehicles controllers (such as vehicle182.2). Moreover, system 180 may include a plurality of external (and/orhome) computing devices 186 and more than two mobile computing devices184 configured to collect and generate customer data (such customerpresence or location information, mobile device and vehicle sensor data,and/or other types of customer data), any suitable number of which beinginterconnected directly to one another and/or via network 130.

In one aspect, each of mobile computing devices 184.1 and 184.2 may beconfigured to communicate with one another directly via peer-to-peer(P2P) wireless communication and/or data transfer. In other aspects,each of mobile computing devices 184.1 and 184.2 may be configured tocommunicate indirectly with one another and/or any suitable device viacommunications over network 130, such as external computing device 186(such as insurance or financial services provider remote servers, or asmart home controller), and/or smart infrastructure component(s) 188,for example. In still other aspects, each of mobile computing devices184.1 and 184.2 may be configured to communicate directly and/orindirectly with other suitable devices, such as remote serversconfigured to collect and analyze customer data (including autoinsurance claim and/or vehicle collision data) to generate customeralerts, which may include synchronous or asynchronous communication.

Each of mobile computing devices 184.1 and 184.2 may be configured tosend data to and/or receive data from one another and/or via network 130using one or more suitable communication protocols, which may be thesame communication protocols or different communication protocols. Forexample, mobile computing devices 184.1 and 184.2 may be configured tocommunicate with one another via a direct radio link 183 a, which mayutilize, for example, a Wi-Fi direct protocol, an ad-hoc cellularcommunication protocol, etc. Mobile computing devices 184.1 and 184.2may also be configured to communicate with vehicles 182.1 and 182.2,respectively, utilizing a BLUETOOTH communication protocol (radio linknot shown). In some embodiments, this may include communication betweena mobile computing device 184.1 and a vehicle controller 181.1. In otherembodiments, it may involve communication between a mobile computingdevice 184.2 and a vehicle telephony, entertainment, navigation, orinformation system (not shown) of the vehicle 182.2 that providesfunctionality other than autonomous (or semi-autonomous) vehiclecontrol. Thus, vehicles 182.2 without autonomous operation features maynonetheless be connected to mobile computing devices 184.2 in order tofacilitate communication, information presentation, or similarnon-control operations (e.g., navigation display, hands-free telephony,or music selection and presentation).

To provide additional examples, mobile computing devices 184.1 and 184.2may be configured to communicate with one another via radio (or radiofrequency) links 183 b and 183 c by each communicating with network 130utilizing a cellular communication protocol. As an additional example,mobile computing devices 184.1 and/or 184.2 may be configured tocommunicate with external computing device (e.g., services providerremote server or a customer smart home controller) 186 via radio links183 b, 183 c, and/or 183 e. Still further, one or more of mobilecomputing devices 184.1 and/or 184.2 may also be configured tocommunicate with one or more smart infrastructure components 188directly (e.g., via radio link 183 d) and/or indirectly (e.g., via radiolinks 183 c and 183 f via network 130) using any suitable communicationprotocols. Similarly, one or more vehicle controllers 181.1 may beconfigured to communicate directly to the network 130 (via radio link183 b) or indirectly through mobile computing device 184.1 (via radiolink 183 b). Vehicle controllers 181.1 may also communicate with othervehicle controllers and/or mobile computing devices 184.2 directly orindirectly through mobile computing device 184.1 via local radio links183 a. As discussed elsewhere herein, network 130 may be implemented asa wireless telephony network (e.g., GSM, CDMA, LTE, etc.), a Wi-Finetwork (e.g., via one or more IEEE 802.11 Standards), a WiMAX network,a Bluetooth network, etc. Thus, links 183 a-183 f may represent wiredlinks, wireless links, or any suitable combination thereof. For example,the links 183 e and/or 183 f may include wired links to the network 130,in addition to, or instead of, wireless radio connections.

In some embodiments, the external computing device 186 may medicatecommunication between the mobile computing devices 184.1 and 184.2 basedupon location or other factors, such as indication of a vehiclecollision occurring or a hazardous area approaching or along a route oftravel. In embodiments in which mobile computing devices 184.1 and 184.2communicate directly with one another in a peer-to-peer fashion, network130 may be bypassed and thus communications between mobile computingdevices 184.1 and 184.2 and external computing device 186 may beunnecessary. For example, in some aspects, mobile computing device 184.1may broadcast geographic location data and/or telematics data directlyto mobile computing device 184.2. In this case, mobile computing device184.2 may operate independently of network 130 to determine vehiclecollision data, location data, operating data, risks associated withoperation, control actions to be taken, which telematics data tobroadcast to other local or remote computing devices, and/or alerts tobe generated at mobile computing device 184.2 based upon the geographiclocation data, hazardous areas locations, customer data, vehicle driver,mobile device user, whether the mobile device user is walking or biking,sensor data, and/or the autonomous operation feature data. In accordancewith such aspects, network 130 and external computing device 186 may beomitted.

However, in other aspects, one or more of mobile computing devices 184.1and/or 184.2 may work in conjunction with external computing device 186to telematics data, vehicle collision data, location data, operatingdata, risks associated with operation, control actions to be taken,and/or alerts to be generated. For example, in some aspects, mobilecomputing device 184.1 may broadcast geographic location data and/orautonomous operation feature data, which is received by externalcomputing device 186. In this case, external computing device 186 may beconfigured to determine whether the same or other information should besent to mobile computing device 184.2 based upon the hazardous areaidentified, the geographic location data, autonomous operation featuredata, or data derived therefrom.

Mobile computing devices 184.1 and 184.2 may be configured to executeone or more algorithms, programs, applications, etc., to determine ageographic location of each respective mobile computing device (and thustheir associated vehicle) to generate, measure, monitor, and/or collectone or more sensor metrics as GPS or telematics data, to broadcast thegeographic data and/or telematics data via their respective radio links,to receive the geographic data and/or telematics data via theirrespective radio links, to determine whether an alert should begenerated based upon the telematics data and/or the geographic locationdata, to generate the one or more alerts, and/or to broadcast one ormore alert notifications to other computing devices.

Such functionality may, in some embodiments be controlled in whole orpart by a Data Application (“App”) operating on the mobile computingdevices 184, as discussed elsewhere herein. Such Data Application maycommunicate between the mobile computing devices 184 and one or moreexternal computing devices 186 (such as servers 140) to facilitatecentralized data collection and/or processing.

In some embodiments, the Data Application may facilitate control of avehicle 182 by a user, such as by selecting vehicle destinations and/orroutes along which the vehicle 182 will travel. The Data Application mayfurther be used to establish restrictions on vehicle use or store userpreferences for vehicle use, such as in a user profile. In furtherembodiments, the Data Application may monitor mobile device or vehicleoperation or, mobile device, vehicle, wearable electronic device, orhome sensor data in real-time to make recommendations or for otherpurposes, such as described herein. The Data Application may furtherfacilitate monitoring and/or assessment of the vehicle 182, such as byevaluating operating data to determine the condition of the vehicle orcomponents thereof (e.g., sensors, autonomous operation features, etc.).

External computing device 186 may be configured to execute varioussoftware applications, algorithms, and/or other suitable programs.External computing device 186 may be implemented as any suitable type ofdevice to facilitate the functionality as described herein. For example,external computing device 186 may be a server 140, as discussedelsewhere herein. As another example, the external computing device 186may be another computing device associated with an operator or owner ofa vehicle 182, such as a desktop or notebook computer. Althoughillustrated as a single device in FIG. 1B, one or more portions ofexternal computing device 186 may be implemented as one or more storagedevices that are physically co-located with external computing device186, or as one or more storage devices utilizing different storagelocations as a shared database structure (e.g. cloud storage).

In some embodiments, external computing device 186 may be configured toperform any suitable portion of the processing functions remotely thathave been outsourced by one or more of mobile computing devices 184.1and/or 184.2 (and/or vehicle controllers 181.1). For example, mobilecomputing device 184.1 and/or 184.2 may collect customer data (e.g.,insurance claim data, vehicle data, vehicle collision data, geographiclocation data, autonomous vehicles system or feature data, and/ortelematics data) as described herein, but may send the data to externalcomputing device 186 for remote processing instead of processing thedata locally. In such embodiments, external computing device 186 mayreceive and process the data to determine whether an anomalous conditionexists and, if so, whether to send an alert notification to one or moremobile computing devices 184.1 and 184.2 or take other actions.

In one aspect, external computing device 186 may additionally oralternatively be part of an insurer computing system (or facilitatecommunications with an insurer computer system), and as such may accessinsurer databases, execute algorithms, execute applications, accessremote servers, communicate with remote processors, etc., as needed toperform insurance-related functions. In aspects in which externalcomputing device 186 facilitates communications with an insurercomputing system (or is part of such a system), data received from oneor more mobile computing devices 184.1-184.N may include usercredentials, which may be verified by external computing device 186 orone or more other external computing devices, servers, etc. These usercredentials may be associated with an insurance profile, which mayinclude, for example, financial account information, insurance policynumbers, a description and/or listing of insured assets, vehicleidentification numbers of insured vehicles, addresses of insuredstructures, contact information, premium rates, discounts, etc. In thisway, data received from one or more mobile computing devices 184.1-184.Nmay allow external computing device 186 to uniquely identify eachinsured customer and/or whether each identified insurance customer hasinstalled the Data Application. In addition, external computing device186 may facilitate the communication of the updated insurance policies,premiums, rates, discounts, etc., to insurance customers for theirreview, modification, and/or approval—such as via wireless communicationor data transmission to one or more mobile computing devices184.1-184.N.

In some aspects, external computing device 186 may facilitate indirectcommunications between one or more of mobile computing devices 184,vehicles 182, smart home controllers, wearable electronic devices,and/or smart infrastructure component 188 via network 130 or anothersuitable communication network, wireless communication channel, and/orwireless link. Smart infrastructure components 188 may be implemented asany suitable type of traffic infrastructure components configured toreceive communications from and/or to send communications to otherdevices, such as mobile computing devices 184 and/or external computingdevice 186. Thus, smart infrastructure components 188 may includeinfrastructure components 126 having infrastructure communicationdevices 124. For example, smart infrastructure component 188 may beimplemented as a smart traffic light, a smart road, a smart railroadcrossing signal, a smart construction notification sign, a roadsidedisplay configured to display messages, a billboard display, a smartbridge, a smart ramp, a smart sign, a parking garage monitoring device,a smart parking lot equipped for wireless communication or datatransmission, etc.

FIG. 2 illustrates a block diagram of an exemplary mobile device 110 oran exemplary on-board computer 114 (and/or smart home controller)consistent with the system 100 and the system 180. The mobile device 110or on-board computer 114 (and/or smart home controller) may include adisplay 202, a GPS unit 206, a communication unit 220, an accelerometer224, one or more additional sensors (not shown), a user-input device(not shown), and/or, like the server 140, a controller 204. In someembodiments, the mobile device 110 and on-board computer 114 (and/orsmart home controller) may be integrated into a single device, or eithermay perform the functions of both. The on-board computer 114 (or mobiledevice 110) interfaces with the sensors 120 to receive informationregarding the vehicle 108 and its environment, which information is usedby the autonomous operation features to operate the vehicle 108.

Similar to the controller 155, the controller 204 may include a programmemory 208, one or more microcontrollers or microprocessors (MP) 210, aRAM 212, and an I/O circuit 216, all of which are interconnected via anaddress/data bus 214. The program memory 208 may include an operatingsystem 226, a data storage 228, a plurality of software applications230, and/or a plurality of software routines 240. The operating system226, for example, may include one of a plurality of general purpose ormobile platforms, such as the Android™, iOS®, or Windows® systems,developed by Google Inc., Apple Inc., and Microsoft Corporation,respectively. Alternatively, the operating system 226 may be a customoperating system designed for autonomous vehicle operation using theon-board computer 114. The data storage 228 may include data such asuser profiles and preferences, application data for the plurality ofapplications 230, routine data for the plurality of routines 240, andother data related to road navigation and/or the autonomous operationfeatures. In some embodiments, the controller 204 may also include, orotherwise be communicatively connected to, other data storage mechanisms(e.g., one or more hard disk drives, optical storage drives, solid statestorage devices, etc.) that reside within the vehicle 108.

As discussed with reference to the controller 155, it should beappreciated that although FIG. 2 depicts only one microprocessor 210,the controller 204 may include multiple microprocessors 210. Similarly,the memory of the controller 204 may include multiple RAMs 212 andmultiple program memories 208. Although FIG. 2 depicts the I/O circuit216 as a single block, the I/O circuit 216 may include a number ofdifferent types of I/O circuits. The controller 204 may implement theRAMs 212 and the program memories 208 as semiconductor memories,magnetically readable memories, or optically readable memories, forexample.

The one or more processors 210 may be adapted and configured to executeany of one or more of the plurality of software applications 230 or anyone or more of the plurality of software routines 240 residing in theprogram memory 204, in addition to other software applications. One ofthe plurality of applications 230 may be an autonomous vehicle operationapplication 232 that may be implemented as a series of machine-readableinstructions for performing the various tasks associated withimplementing one or more of the autonomous operation features accordingto the autonomous vehicle operation method 300. Another of the pluralityof applications 230 may be an autonomous communication application 234that may be implemented as a series of machine-readable instructions fortransmitting and receiving autonomous operation information to or fromexternal sources via the communication module 220. Still anotherapplication of the plurality of applications 230 may include anautonomous operation monitoring application 236 that may be implementedas a series of machine-readable instructions for sending informationregarding autonomous operation of the vehicle to the server 140 via thenetwork 130.

The plurality of software applications 230 may call various of theplurality of software routines 240 to perform functions relating toautonomous vehicle operation, monitoring, or communication. One of theplurality of software routines 240 may be a configuration routine 242 toreceive settings from the vehicle operator to configure the operatingparameters of an autonomous operation feature. Another of the pluralityof software routines 240 may be a sensor control routine 244 to transmitinstructions to a sensor 120 and receive data from the sensor 120. Stillanother of the plurality of software routines 240 may be an autonomouscontrol routine 246 that performs a type of autonomous control, such ascollision avoidance, lane centering, or speed control. In someembodiments, the autonomous vehicle operation application 232 may causea plurality of autonomous control routines 246 to determine controlactions required for autonomous vehicle operation.

Similarly, one of the plurality of software routines 240 may be amonitoring and reporting routine 248 that transmits informationregarding autonomous vehicle operation to the server 140 via the network130. Yet another of the plurality of software routines 240 may be anautonomous communication routine 250 for receiving and transmittinginformation between the vehicle 108 and external sources to improve theeffectiveness of the autonomous operation features. Any of the pluralityof software applications 230 may be designed to operate independently ofthe software applications 230 or in conjunction with the softwareapplications 230.

In addition to connections to the sensors 120 that are external to themobile device 110 or the on-board computer 114, the mobile device 110 orthe on-board computer 114 may include additional sensors 120, such asthe GPS unit 206 or the accelerometer 224, which may provide informationregarding the vehicle 108 for autonomous operation and other purposes.Such sensors 120 may further include one or more sensors of a sensorarray 225, which may include, for example, one or more cameras,accelerometers, gyroscopes, magnetometers, barometers, thermometers,proximity sensors, light sensors, Hall Effect sensors, radar units, etc.The one or more sensors of the sensor array 225 may be positioned todetermine telematics data regarding the speed, force, heading, and/ordirection associated with movements of the vehicle 108. Furthermore, thecommunication unit 220 may communicate with other autonomous vehicles,infrastructure, or other external sources of information to transmit andreceive information relating to autonomous vehicle operation. Thecommunication unit 220 may communicate with the external sources via thenetwork 130 or via any suitable wireless communication protocol network,such as wireless telephony (e.g., GSM, CDMA, LTE, etc.), Wi-Fi (802.11standards), WiMAX, Bluetooth, infrared or radio frequency communication,etc.

Furthermore, the communication unit 220 may provide input signals to thecontroller 204 via the I/O circuit 216. The communication unit 220 mayalso transmit sensor data, device status information, control signals,or other output from the controller 204 to one or more external sensorswithin the vehicle 108, mobile devices 110, on-board computers 114, orservers 140.

The mobile device 110 or the on-board computer 114 may include auser-input device (not shown) for receiving instructions or informationfrom the vehicle operator, such as settings relating to an autonomousoperation feature. The user-input device (not shown) may include a“soft” keyboard that is displayed on the display 202, an externalhardware keyboard communicating via a wired or a wireless connection(e.g., a Bluetooth keyboard), an external mouse, a microphone, or anyother suitable user-input device. The user-input device (not shown) mayalso include a microphone capable of receiving user voice input.

The computing devices of FIGS. 1 and 2 may be configured to employmachine or cognitive learning techniques, and/or configured to performthe functionality discussed elsewhere herein, including configured to(1) identify hazardous areas using customer data, such as mobile device(e.g., smart phones, smart glasses, smart watches, smart wearabledevices, smart contact lenses, smart cameras, and/or other devicescapable of wireless communication), interconnected home, and/or smartvehicle data, the customer data may be associated with, or collectedduring, vehicle, bicycle, or pedestrian daily or normal travel and/orgenerated or acquired before, during, or after vehicle collisions ornear collisions; (2) identify emerging hazardous areas using real-timecustomer data; (3) generate and/or update electronic or virtualnavigation maps that depict or identify known or emerging hazardousareas; (4) determine a reason or cause why each hazardous areas ishazardous (such as construction area, intersection, poorly timed trafficlights, confusing traffic flow, poorly designed on-ramps or exit ramps,traffic merging issues, inoperable traffic lights or hidden trafficsigns, etc.); (5) re-route vehicles, pedestrians, or bicyclists aroundhazardous areas; (6) generate alerts when approaching hazardous areas;and/or (7) identify alternate and lower risk parking lots in thevicinity, or within a predetermined distance, of a destination.

Machine Learning

Machine learning techniques have been developed that allow parametric ornonparametric statistical analysis of large quantities of data. Suchmachine learning techniques may be used to automatically identifyrelevant variables (i.e., variables having statistical significance or asufficient degree of explanatory power) from data sets. This may includeidentifying relevant variables or estimating the effect of suchvariables that indicate actual observations in the data set. This mayalso include identifying latent variables not directly observed in thedata, viz. variables inferred from the observed data points. In someembodiments, the methods and systems described herein may use machinelearning techniques to identify and estimate the effects of observed orlatent variables such as customer location, time of day, type of vehiclecollision, type of vehicle damage or personal injury, vehicle collisionlocation, amount of vehicle damage or medical expenses associated with avehicle collision, or other such variables that influence the risksassociated with vehicle collisions or vehicle travel.

Some embodiments described herein may include automated machine learningto determine hazardous areas (e.g., roads, road segments, intersections,parking lots, exit ramps, etc.); determine risk levels of the hazardousareas; identify relevant risk factors of the hazardous areas; optimizevehicle, bicycle, or pedestrian routes to avoid hazardous areas;generate or update electronic or virtual navigation maps; generatealerts to vehicles, drivers, bikers, or pedestrians; automaticallyengage or disengage autonomous features; determine which autonomousfeatures should be preferably engaged or disengaged for each hazardousarea or type of hazardous area; determine common causes of vehiclecollisions at each hazardous area; generate driving simulationsinvolving the common causes being simulated virtually at each hazardousarea location and environment; program smart infrastructure to generateelectronic alerts or warnings to vehicles or mobile devices travelingtoo fast when approaching the hazardous area; and/or perform otherfunctionality as described elsewhere herein.

Although the methods described elsewhere herein may not directly mentionmachine learning techniques, such methods may be read to include suchmachine learning for any determination or processing of data that may beaccomplished using such techniques. In some embodiments, suchmachine-learning techniques may be implemented automatically uponoccurrence of certain events or upon certain conditions being met. Useof machine learning techniques, as described herein, may begin withtraining a machine learning program, or such techniques may begin with apreviously trained machine learning program.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Machine learningmay involve identifying and recognizing patterns in existing data (suchas vehicle collisions being caused by the same thing repeatedlyoccurring at one or more hazardous areas or location), in order tofacilitate making predictions based upon subsequent customer data.Models may be created based upon example inputs of data in order to makevalid and reliable predictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as mobile device, vehicle, or smart infrastructure sensor and/orcontrol signal data, and other data discussed herein. The machinelearning programs may utilize deep learning algorithms that areprimarily focused on pattern recognition, and may be trained afterprocessing multiple examples. The machine learning programs may includeBayesian program learning (BPL), voice recognition and synthesis, imageor object recognition, optical character recognition, and/or naturallanguage processing—either individually or in combination. The machinelearning programs may also include natural language processing, semanticanalysis, automatic reasoning, and/or machine learning.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct or a preferredoutput. In unsupervised machine learning, the processing element may berequired to find its own structure in unlabeled example inputs. In oneembodiment, machine learning techniques may be used to extract thecontrol signals generated by computer systems or sensors, and under whatconditions those control signals were generated.

The machine learning programs may be trained with vehicle-mounted,home-mounted, and/or mobile device-mounted sensor data to identifycertain customer activity, such as routine travel through one or morehazardous areas at certain times of day to determine whether a giventype of vehicle collision (e.g., collision causing vehicle damage of apredetermined amount, or causing one or more pedestrian injuries) may bemore likely than normal at a specific location, and/or monitoringvehicle behavior as the vehicle travels through the hazardous area,whether under self-control or manual control.

After training, machine learning programs (or information generated bysuch machine learning programs) may be used to evaluate additional data.Such training data may be related to past and/or historical vehiclecollisions or near collisions gathered by smart vehicles, mobile device,or smart infrastructure, or other similar data to be analyzed orprocessed. The trained machine learning programs (or programs utilizingmodels, parameters, or other data produced through the training process)may then be used for determining, assessing, analyzing, predicting,estimating, evaluating, or otherwise processing new data not included inthe training data. Such new or additional data may be related tocurrent, up-to-date, or real-time vehicle collisions or near collisionsgathered by smart vehicles, mobile device, smart infrastructure, orother sensors and cameras, or other similar data to be analyzed orprocessed. Such trained machine learning programs may, thus, be used toperform part or all of the analytical functions of the methods describedelsewhere herein.

Hazardous Area Identification & Re-Routing

FIG. 3A illustrates an exemplary computer-implemented method of usingauto insurance claim data and/or other customer data to identifyhazardous areas and re-route autonomous vehicles to reduce futurevehicle collisions 300. The method 300 may be implemented via one ormore processors, transceivers, and/or sensors, and/or viacomputer-executable instructions stored on non-transitorycomputer-readable medium or media.

The method 300 may include, via one or more (local or remote)processors, transceivers, and/or sensors, (1) analyzing auto insuranceclaim data to identify hazardous areas and/or classify the hazardousareas 302. For instance historic or past data from actual auto insuranceclaims may be analyzed, including images of damaged vehicles. The autoinsurance claim data may include a location of a vehicle collision,number of vehicles involved, cause of collision, extent and type ofvehicle damage, cost of vehicle repair or replacement, number of injuredpassengers or pedestrians, etc. The hazardous areas may be classified bytype of vehicle damage, cost of vehicle repairs, number of injuries,cost of medical expenses, whether pedestrians or bicyclists wereinvolved, location, type of road (such as intersection, circular trafficpattern, on-ramp, off-ramp, merging traffic from right or left, corner,parking lot, etc.).

The method 300 may include, via the one or more processors,transceivers, and/or sensors, (2) building or generating a virtualnavigation (or road) map that depicts or virtually represents thehazardous areas 304. For instance, icons representing or indicatinghazardous areas (such as circular icons) may be superimposed upon anexisting virtual road map.

The method 300 may include, via the one or more processors,transceivers, and/or sensors, (3) transmitting the virtual navigationmap to one or more autonomous vehicles 306. The autonomous vehicles 306may use the virtual navigation map for routing between originationpoints and destination points. For instance, virtual routes maygenerated for routing the vehicle to travel in autonomous mode or undermanual operation/control to destination or through a hazardous area.

The method 300 may include, via the one or more processors,transceivers, and/or sensors, (4) determining if the current autonomousvehicle route includes traveling through a hazardous area marked on thevirtual map 308. Additionally or alternatively, it may be determinedwhether the autonomous vehicle is travelling on a road that has ahazardous area along it and/or that is in the direction of currentautonomous vehicle, and/or whether the autonomous vehicle is within apredetermined distance of the hazardous area (e.g., within 1, 2, or 5miles).

If so, the method 300 may include, via the one or more processors,transceivers, and/or sensors, (5) determining a type of the hazardousarea, and based upon the type of the hazardous area, then determiningwhether to re-route the autonomous vehicle to avoid the hazardous area310. For instance, if the hazardous area is an intersection that is highrisk due to a blind spot that negatively impacts manual driving, theautonomous vehicle may be equipped with autonomous features or systemsthat negate the risk. If so, the autonomous functionality may beautomatically engaged prior to entering or traveling the hazardous area,and the autonomous vehicle will not be re-routed. On the other hand, ifthe autonomous functionality has been found not to negate the specifictype of risk (such as via vehicle testing and/or vehicle collision dataanalysis), then an alternate route may be determined that avoids thehazardous area, and the autonomous vehicle may be re-routed.Additionally or alternatively, if a hazardous area of any specific type,or even of any type, is approaching, (i) an autonomous vehicle may beautomatically re-routed, or (ii) an alternate route may be presented andrecommended to a human driver, such as via a navigation unit, thatavoids the hazardous area.

The method 300 may include, via the one or more processors,transceivers, and/or sensors, (6) receiving telematics data from theautonomous vehicle indicating vehicle re-routing around hazardous areas312. For instance, the telematics data may indicate how often anautonomous vehicle avoids a hazardous area, travels through a hazardousarea, and/or what autonomous systems or features are operating whiletraversing through hazardous areas.

The method 300 may include, via the one or more processors,transceivers, and/or sensors, (7) updating an auto insurance premium ordiscount 314. For instance, autonomous vehicles or vehicle owners thatdisplay risk averse driving behavior and avoid hazardous areas, orchoose an alternative, less risk-prone mode of travel, may be rewardedwith lower premiums or higher discounts on auto or other types ofinsurance. The method 300 may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In one aspect, a computer-implemented method of reducing vehiclecollisions may be provided. The method may include (1) analyzing, viaone or more processors, auto insurance claim data to identify hazardousareas (the hazardous areas being defined, at least in part, by GPSlocation or GPS coordinates); (2) building or generating, via the one ormore processors, a virtual navigation map of roads with the hazardousareas being visually depicted; (3) transmitting, via the one or moreprocessors and/or transceivers, and via wireless communication or datatransmission over one or more radio frequency links or wirelesscommunication channels, the virtual navigation map to a vehiclenavigation system of a customer smart or autonomous vehicle tofacilitate the vehicle navigation system re-routing the customer vehiclearound the hazardous area(s); (4) receiving, via the one or moreprocessors and/or transceivers, and via wireless communication or datatransmission over one or more radio frequency links or wirelesscommunication channels, an indication that the customer vehiclere-routed around one or more hazardous areas; (5) generating oradjusting, via the one or more processors, an auto insurance premium ordiscount based upon the customer vehicle re-routing around the one ormore hazardous areas; and/or (6) transmitting, via the one or moreprocessors and/or transceivers, and via wireless communication or datatransmission over one or more radio frequency links or wirelesscommunication channels, the adjusted auto insurance premium or discountto a customer mobile device or to the customer vehicle for customerreview to incentivize safer vehicle operation.

A hazardous area may be a high risk intersection at an above-averagerisk of vehicle collision, a high risk portion of a road that isassociated with an above-average risk of vehicle collision, or a highrisk parking lot associated with an average risk of vehicle collision ortheft.

The method may include analyzing, via one or more processors, autonomousvehicle sensor data and/or vehicle camera image data to identify thehazardous areas and/or determine a cause of the vehicle collision. Themethod may include analyzing, via one or more processors, smartinfrastructure sensor data to identify the hazardous areas and/ordetermine a cause of the vehicle collision. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.\

In another aspect, a computer system configured to reduce vehiclecollisions may be provided. The computer system may include one or moreprocessors, transceivers, servers, and/or sensors configured to: (1)analyze auto insurance claim data to identify hazardous areas (thehazardous areas being defined, at least in part, by GPS location or GPScoordinates); (2) build or generate a virtual navigation map of roadswith the hazardous areas being visually depicted; (3) transmit, viawireless communication or data transmission over one or more radiofrequency links or wireless communication channels, the virtualnavigation map to a vehicle navigation system of customer smart orautonomous vehicle to facilitate the vehicle navigation systemre-routing the customer vehicle around the hazardous areas; (4) receive,via wireless communication or data transmission over one or more radiofrequency links or wireless communication channels, an indication thatthe customer vehicle re-routed around one or more hazardous areas; (5)generate or adjust an auto insurance premium or discount based upon thecustomer vehicle re-routing around the one or more hazardous areas;and/or (6) transmit, via wireless communication or data transmissionover one or more radio frequency links or wireless communicationchannels, the adjusted auto insurance premium or discount to a customermobile device or to the customer vehicle for customer review toincentivize safer vehicle operation. The computer system may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

In another aspect, a computer-implemented method of reducing vehiclecollisions may be provided. The method may include (1) retrieving, viaone or more processors, auto insurance claim data and/or vehiclecollision data that includes GPS or location data from a memory unit orreceiving, via one or more processors and/or transceivers, the autoinsurance claim data and/or vehicle collision data that includes GPS orlocation data transmitted from an agent or customer mobile device orvehicle (via wireless communication or data transmission over one ormore radio links and/or wireless communication channels); (2) analyzing,via the one or more processors, the auto insurance claim data and/orvehicle collision data that includes GPS or location data to identifyhazardous areas and associated locations thereof (the hazardous areasbeing defined, at least in part, by GPS location or GPS coordinates);(3) building or generating, via the one or more processors, a virtualnavigation road map or navigation map virtual overlay of hazardous areasbeing visually depicted; (4) transmitting, via the one or moreprocessors and/or transceivers, and via wireless communication or datatransmission over one or more radio frequency links or wirelesscommunication channels, the virtual navigation map and/or overlay to avehicle navigation system of a customer smart or autonomous vehicle tofacilitate the vehicle navigation system updating its navigation roadmap with the overlay and then re-routing the customer vehicle around thehazardous areas; (5) receiving, via the one or more processors and/ortransceivers, and via wireless communication or data transmission overone or more radio frequency links or wireless communication channels, anindication that the customer vehicle re-routed around one or morehazardous areas, a virtual log of routes that the vehicle has taken,and/or an indication of hazardous areas that the vehicle has traveledthrough and/or avoided; (6) generating or adjusting, via the one or moreprocessors, an auto insurance premium or discount based upon thecustomer vehicle re-routing around the one or more hazardous areas orvirtual logs of routes taken; and/or (7) transmitting, via the one ormore processors and/or transceivers, and via wireless communication ordata transmission over one or more radio frequency links or wirelesscommunication channels, the adjusted auto insurance premium or discountto a customer mobile device or to the customer vehicle for customerreview to incentivize safer vehicle operation. The method may includeadditional, less, or alternate functionality, including that discussedelsewhere herein, and may be implemented via one or more local or remoteprocessors, transceivers, servers, and sensors, and/or non-transitorycomputer-readable memory units storing computer-executable instructions.

Updating Autonomous Vehicle Navigation Maps

FIG. 3B illustrates an exemplary computer-implemented method of usingauto insurance claim data and/or other customer data to identifyhazardous areas and re-route autonomous vehicles to reduce futurevehicle collisions 350. The method 350 may be implemented via one ormore processors, transceivers, servers, and/or sensors, and/or viacomputer-executable instructions stored on non-transitorycomputer-readable medium or media.

The method 350 may include, via one or more processors, transceivers,servers, and/or sensors, (1) downloading an initial virtual navigationmap into an autonomous vehicle controller or smart vehicle navigationmap 352; (2) receiving a virtual navigation map or information detailinghazardous areas identified by analyzing auto insurance claim and/orvehicle collision data (such as discussed elsewhere herein) 354; and/or(3) updating the virtual navigation map to depict the hazardous areasidentified 356.

The method 350 may include, via one or more processors, transceivers,servers, and/or sensors, (4) determining if the current autonomousvehicle route includes traveling through a hazardous area marked on thevirtual map 358; and (5) if so, determining a type of the hazardousarea, and based upon the type of the hazardous area, determining whetherto re-route the autonomous vehicle to avoid the hazardous area 360. Themethod 350 may also include determining whether or not to engage ordisengage autonomous features or systems prior to the vehicle reachingthe hazardous area, such as discussed elsewhere herein.

The method 350 may include, via one or more processors, transceivers,servers, and/or sensors, (6) building a virtual log of autonomousvehicle data indicating re-routing or routing around hazardous areas362. The virtual log may include telematics data and/or routes taken bythe autonomous vehicle, and how often an autonomous vehicle avoids ahazardous area or travels through a hazardous area, and what autonomoussystems or features are operating while traversing through hazardousareas.

The method 350 may include, via one or more processors, transceivers,and/or sensors, (7) transmitting the virtual log to an insuranceprovider remote server to facilitate the remote server updating an autoinsurance premium or discount 364. For instance, autonomous vehicles orother vehicle owners that display risk averse driving behavior and avoidhazardous areas, or choose an alternative, less risk-prone mode oftravel, may be rewarded with lower premiums or higher discounts on auto,life, health, personal, or other types of insurance. The method 350 mayinclude additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In one aspect, a computer-implemented method of reducing vehiclecollisions may be provided. The method including (1) downloading, viaone or more processors, an initial navigation road map into anautonomous vehicle controller (or navigation system); (2) receiving, viaone or more processors and/or transceivers, (i) auto insurance claimdata and/or vehicle collision data that includes GPS or location data,and/or (ii) an updated vehicle navigation map that includes hazardousareas depicted, transmitted from an insurance provider remote server orsmart infrastructure (via wireless communication or data transmissionover one or more radio links and/or wireless communication channels);(3) updating, via the one or more processors, the navigation road map ofthe autonomous vehicle controller (or navigation system) to reflecthazardous areas indicating by the auto insurance claim data and/orvehicle collision data, and/or updating the navigation map to includehazardous areas and associated locations thereof (the hazardous areasbeing defined, at least in part, by GPS location or GPS coordinates);(4) using, via the one or more processors, the updated navigation mapand/or hazardous area GPS location to identify that the autonomousvehicle is traveling along a current route to a destination that travelsthrough a hazardous area identified by the updated navigation map(and/or auto insurance claim or vehicle collision data); and/or (5) ifso, determining, via the one or more processors, an alternate route forthe autonomous vehicle to travel to its destination that avoids thehazardous area to facilitate reducing vehicle collisions.

The method may include generating, via the one or more processors, avirtual log of routes that the vehicle has taken, and/or an indicationof hazardous areas that the autonomous vehicle has traveled throughand/or avoided; and/or transmitting, via the one or more processors, thevirtual log to an insurance provider remote server to facilitate theremote server adjusting an auto insurance premium or discount based uponthe customer vehicle re-routing around the one or more hazardous areas,or virtual logs of routes taken. The method may include, via the one ormore processors and/or transceivers, and via wireless communication ordata transmission over one or more radio frequency links or wirelesscommunication channels, receiving and displaying the adjusted autoinsurance premium or discount on a customer mobile device or customervehicle display for customer review to incentivize safer vehicleoperation.

The hazardous area may be a high risk intersection at an above-averagerisk of vehicle collision, a high risk portion of a road that isassociated with an above-average risk of vehicle collision, a high riskparking lot that is associated with an above-average risk of theft orvehicle collision, a high risk portion of a road that is associated witha circular traffic pattern, and/or other hazardous areas, includingthose discussed elsewhere herein. The method may include analyzing, viaone or more processors, autonomous vehicle sensor data and/or vehiclecamera image data to identify and update the hazardous areas, and/ordetermine a cause of the vehicle collision. The method may includeanalyzing, via one or more processors, smart infrastructure sensor datato identify and/or update the hazardous areas, and/or determine a causeof the vehicle collision. The method may include additional, less, oralternate functionality, including that discussed elsewhere herein, andmay be implemented via one or more local or remote processors,transceivers, servers, and/or sensors, and/or via non-transitorycomputer-readable memory units storing computer-executable instructions.

Autonomous Feature Engagement & Disengagement

FIG. 4 illustrates an exemplary computer-implemented method ofidentifying hazardous areas and determining whether to engage ordisengage autonomous features prior to an autonomous vehicle entering orpassing through the hazardous area 400. The method 400 may beimplemented via one or more processors, transceivers, servers, and/orsensors, and/or via computer-executable instructions stored onnon-transitory computer-readable medium or media.

The method 400 may include (1) analyzing auto insurance claim data toidentify hazardous areas, and/or autonomous features or systems thatimpacted vehicle collision severity 402. For instance, vehiclecollisions involving autonomous vehicle with certain autonomous featuresor systems may be analyzed to determine which autonomous features orsystems lower risk or lower vehicle damage, or passenger or pedestrianinjury, as well those that may not impact risk or the amount/extent ofdamage or injuries.

The method 400 may include (2) building a virtual navigation mapdepicting the hazardous areas 404, and transmitting the virtualnavigation map to autonomous vehicles and/or vehicle navigation unitsfor vehicle routing 406. The method 400 may include (3) determining ifthe current autonomous vehicle route includes traveling through ahazardous area 408.

If so, the method 400 may include (4) determining if the hazardous arearisk may be minimized by autonomous feature engagement or disengagement410. For instance, depending upon the type of hazardous area (corner,intersection, highway ramp, merging traffic, abnormal traffic pattern,road construction, etc.) and the type of autonomous feature, (5) theautonomous feature may automatically engage 412 prior to entering apredetermined distance of the hazardous area or a recommendation may begenerated alerting the human driver that autonomous feature use isrecommended while traversing the hazardous area. Additionally oralternatively, depending upon the type of hazardous area (corner,intersection, highway ramp, merging traffic, abnormal traffic pattern,road construction, etc.) and the type of autonomous feature, it may berecommended to that the human in the driver's seat retake manual controlof the vehicle when ready 412 and manual operate the vehicle through thehazardous area if testing has determined that the autonomous feature(s)of the autonomous vehicle increase risk for the type of hazardous areaencountered.

The method 400 may include (6) receiving telematics data from theautonomous vehicle indicating autonomous feature engagement ordisengagement at or while traversing the hazardous areas 414. The method400 may include (7) adjusting an auto, health, life, personal, or othertype of insurance premium or discount 416 to reflect risk averse vehicleowners that follow recommendations to either engage or disengageautonomous features when approaching and traversing hazardous areas. Themethod 400 may include additional, less, or alternate functionality,including that discussed elsewhere herein.

In one aspect, a computer-implemented method of reducing vehiclecollisions may be provided. The method may include (1) analyzing, viaone or more processors, auto insurance claim data to identify hazardousareas (the hazardous areas being defined by GPS location or GPScoordinates); (2) building or generating, via the one or moreprocessors, a virtual navigation map of roads with the hazardous areasbeing visually depicted; (3) transmitting, via the one or moreprocessors and/or transceivers, and via wireless communication or datatransmission over one or more radio frequency links or wirelesscommunication channels, the virtual navigation map to a vehiclenavigation or control system of a customer autonomous or semi-autonomousvehicle to facilitate the vehicle navigation or control systemautomatically engaging a vehicle self-driving feature or system as theautonomous vehicle approaches and drives through one or more hazardousareas; (4) receiving, via the one or more processors and/ortransceivers, and via wireless communication or data transmission overone or more radio frequency links or wireless communication channels, anindication that the autonomous vehicle engaged self-drivingfunctionality as the autonomous vehicle traveled through the one or morehazardous areas; (5) generating or adjusting, via the one or moreprocessors, an auto insurance premium or discount based upon theautonomous vehicle self-driving through the one or more hazardous areas;and/or (6) transmitting, via the one or more processors and/ortransceivers, and via wireless communication or data transmission overone or more radio frequency links or wireless communication channels,the adjusted auto insurance premium or discount to a customer mobiledevice or to the customer vehicle for customer review to incentivizeautonomous vehicle self-driving through hazardous areas.

In another aspect, a computer-implemented method of reducing vehiclecollisions may be provided. The method may include (1) analyzing, viaone or more processors, auto insurance claim data to identify hazardousareas (the hazardous areas being defined by GPS location or GPScoordinates); (2) building or generating, via the one or moreprocessors, a virtual navigation map of roads with the hazardous areasbeing visually depicted; (3) transmitting, via the one or moreprocessors and/or transceivers, and via wireless communication or datatransmission over one or more radio frequency links or wirelesscommunication channels, the virtual navigation map to a vehiclenavigation or control system of a customer autonomous or semi-autonomousvehicle to facilitate the vehicle navigation or control systemautomatically dis-engaging a vehicle self-driving feature or system asthe autonomous vehicle approaches and drives through one or morehazardous areas or recommending manual vehicle operation while travelingthrough the one or more hazardous areas; (4) receiving, via the one ormore processors and/or transceivers, and via wireless communication ordata transmission over one or more radio frequency links or wirelesscommunication channels, an indication that the autonomous vehicledis-engaged self-driving functionality and/or was driven under manualcontrol or direction as the autonomous vehicle traveled through the oneor more hazardous areas; (5) generating or adjusting, via the one ormore processors, an auto insurance premium or discount based upon theautonomous vehicle being manually driven through the one or morehazardous areas; and/or (6) transmitting, via the one or more processorsand/or transceivers, and via wireless communication or data transmissionover one or more radio frequency links or wireless communicationchannels, the adjusted auto insurance premium or discount to a customermobile device or to the customer vehicle for customer review toincentivize lower risk vehicle operation of autonomous vehiclestraveling through hazardous areas.

In another aspect, a computer-implemented method of reducing vehiclecollisions may be provided. The method may include (1) analyzing, viaone or more processors, auto insurance claim data and/or vehiclecollision data to identify hazardous areas, the hazardous areas beingassociated with vehicle collisions involving vehicle damage above apredetermined amount (or vehicle damage or needed repairs above apredetermined threshold), each hazardous area being defined, at least inpart, by GPS location or GPS coordinates; (2) building or generating,via the one or more processors, a virtual navigation map of roads withthe hazardous areas being visually depicted; (3) and/or transmitting,via the one or more processors and/or transceivers, and via wirelesscommunication or data transmission over one or more radio frequencylinks or wireless communication channels, the virtual navigation map toa vehicle navigation or control system of a customer autonomous orsemi-autonomous vehicle to facilitate the vehicle navigation or controlsystem automatically engaging (or disengaging with manual operatorpermission and awareness) a vehicle self-driving feature or system asthe autonomous vehicle approaches and drives through one or morehazardous areas.

The method may include (4) receiving, via the one or more processorsand/or transceivers, and via wireless communication or data transmissionover one or more radio frequency links or wireless communicationchannels, an indication that the autonomous vehicle engaged self-drivingfunctionality as the autonomous vehicle traveled through the one or morehazardous areas (or virtual log of such information); and/or (5)generating or adjusting, via the one or more processors, an autoinsurance premium or discount based upon the autonomous vehicleself-driving through the one or more hazardous areas. The method mayinclude (6) transmitting, via the one or more processors and/ortransceivers, and via wireless communication or data transmission overone or more radio frequency links or wireless communication channels,the adjusted auto insurance premium or discount to a customer mobiledevice or to the customer vehicle for customer review to incentivizeautonomous vehicles self-driving through hazardous areas.

Analyzing, via one or more processors, the auto insurance claim data mayinvolve analyzing past or historic auto insurance claim data to identifyhazardous areas, such as intersections, road segments, circular orabnormal traffic patterns, highway exit ramps or on-ramps, or parkinglots with an abnormal or above average risk of vehicle collision.Analyzing, via one or more processors, the auto insurance claim data mayinvolve analyzing past or historic auto insurance claim data to identifyhazardous areas associated with auto insurance claims above apredetermined threshold or amount, or total loss vehicles. Analyzing,via one or more processors, the auto insurance claim data may involveanalyzing past or historic auto insurance claim data to identifyhazardous areas associated with vehicle collisions resulting in vehicledamage requiring auto repairs above a predetermined threshold or amount(such as $500, or $2,000).

Analyzing, via one or more processors, the vehicle collision data mayinvolve analyzing current or up-to-date auto insurance claim data,telematics data, vehicle sensor data, and/or autonomous vehicle data toidentify hazardous areas, such as intersections, road segments, circularor abnormal traffic patterns, highway exit or on ramps, or parking lotswith an abnormal or above average risk of vehicle collision. Analyzing,via one or more processors, the auto insurance claim data may involveanalyzing current or up-to-date auto insurance claim data, telematicsdata, vehicle sensor data, and/or autonomous vehicle data to identifyhazardous areas associated with auto insurance claims above apredetermined threshold or amount. Analyzing, via one or moreprocessors, the auto insurance claim data may involve analyzing currentor up-to-date auto insurance claim data, telematics data, vehicle sensordata, and/or autonomous vehicle data to identify hazardous areasassociated with vehicle collisions resulting in vehicle damage requiringauto repairs above a predetermined threshold or amount (such as $500, or$2,000).

The method may further include analyzing the auto insurance claim dataand/or vehicle collision data to identify autonomous feature performanceof autonomous vehicles involved with the hazardous areas, anddetermining whether or not each type of autonomous feature should beengaged or disengaged prior to an autonomous vehicle having that type ofautonomous feature entering or approaching a hazardous area (based uponeach autonomous feature performance at each hazardous area, such asdetermined from past auto insurance claim or past vehicle collisiondata). The foregoing methods may include additional, less, or alternateactions, including those discussed elsewhere herein, and may beimplemented via computer systems comprising processors, transceivers,servers, and/or sensors.

In another aspect, a computer system configured to reduce vehiclecollisions may be provided. The computer system may include one or moreprocessors, transceivers, servers, and/or sensors configured to: (1)retrieve auto insurance claim data and/or vehicle collision data fromone or more memory units, or receive the auto insurance claim dataand/or vehicle collision data via wireless communication or datatransmission over one or more radio links or wireless communicationchannels; (2) analyze the auto insurance claim data and/or vehiclecollision data to identify hazardous areas, the hazardous areas beingassociated with vehicle collisions involving vehicle damage above apredetermined amount (or vehicle damage or needed repairs above apredetermined threshold or dollar amount), each hazardous areas beingdefined by GPS location or GPS coordinates; (3) build or generate avirtual navigation map of roads with the hazardous areas being visuallydepicted; and/or (4) transmit, via wireless communication or datatransmission over one or more radio frequency links or wirelesscommunication channels, the virtual navigation map to a vehiclenavigation unit or control system of a customer autonomous orsemi-autonomous vehicle to facilitate the vehicle navigation unit orcontrol system automatically engaging (or with customer permission andawareness, disengaging) a vehicle self-driving feature or system as theautonomous vehicle approaches and drives through one or more hazardousareas. The computer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

Building Hazardous Area Virtual Maps & Alert Generation

FIG. 5 illustrates an exemplary computer-implemented method of buildinghazardous area virtual maps and generating alerts for bicyclist orpedestrian wearable electronics 500. The method 500 may be implementedvia one or more local or remote processors, transceivers, servers,and/or sensors, and/or via computer-executable instructions stored onnon-transitory computer-readable medium or media.

The method 500 may include (1) analyzing auto insurance claim data toidentify hazardous areas and/or classify hazardous areas 502. Forinstance historic or past data from actual auto insurance claims may beanalyzed, including images of damaged vehicles. The auto insurance claimdata may include a location of a vehicle collision, number of vehiclesinvolved, cause of collision, extent and type of vehicle damage, cost ofvehicle repair or replacement, number of injured passengers orpedestrians, type of injuries, etc. The hazardous areas may beclassified by type of vehicle damage, cost of vehicle repairs, numberand type of injuries, whether pedestrians or bicyclists were involved,location, and/or type of road (such as intersection, circular trafficpattern, on-ramp, off-ramp, merging traffic from right or left, corner,etc.).

The method 500 may include (2) building or generating a virtualnavigation (or road) map that depicts or virtually represents thehazardous areas 504. For instance, icons representing or indicatinghazardous areas (such as circular icons) may be superimposed upon anexisting virtual road map.

The method 500 may include (3) transmitting the virtual navigation mapto wearable electronics associated with users (such as smart watches orglasses), or user mobile devices. The method 500 may include (4)determining if a current (GPS) location of the wearable electronicsdevice is within a predetermined distance of, or on route to, ahazardous area 508. For instance, it may be determined that a wearabledevice on a biker or pedestrian is approaching a high risk intersectionor corner. If so, the method 500 may include (5) determining whether thehazardous area is of a certain type (such as an intersection, one waystreet, abnormal traffic pattern or merging traffic, etc.), and/orgenerate an electronic alert to notify the pedestrian or bicyclist ofthe approaching hazardous area 510. Also, recommendations may begenerated and presented via the wearable devices to allow the user timeto avoid the hazardous areas, and/or alternate routes may be generatedand presented via the wearable devices.

The method 500 may include (6) receiving an indication that thepedestrian or bicyclist followed a recommendation or alternate route toavoid one or more hazardous areas 512. The method 500 may include (7)updating an auto, life, health, personal, or other type of insurancepremium or discount to reflect risk averse behavior by the user orinsured 514. The method 500 may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In one aspect, a computer-implemented method of reducing vehiclecollisions may be provided. The method may include (1) retrieving, viaone or more processors, auto insurance claim data and/or vehiclecollision data that includes GPS or location data from a memory unit orreceiving, via one or more processors and/or transceivers, the autoinsurance claim data and/or vehicle collision data that includes GPS orlocation data transmitted from an agent or customer mobile device orvehicle (via wireless communication or data transmission over one ormore radio links and/or wireless communication channels); (2) analyzing,via the one or more processors, the auto insurance claim data and/orvehicle collision data that includes GPS or location data to identifyhazardous areas and associated locations thereof (the hazardous areasbeing defined by, at least in part, GPS location or GPS coordinates),the hazardous areas being associated with vehicle collisions thatinvolve one or more pedestrians, and/or one or more bicyclists; (3)building or generating, via the one or more processors, a virtualnavigation road map or navigation map virtual overlay of hazardous areasbeing visually depicted; (4) transmitting, via the one or moreprocessors and/or transceivers, and via wireless communication or datatransmission over one or more radio frequency links or wirelesscommunication channels, the virtual navigation map and/or overlay to acustomer mobile device and/or wearable electronics device to facilitatea navigation application (App) stored thereon to update itself with theoverlay and then re-route customers around the hazardous areas; (5)receiving, via the one or more processors and/or transceivers, and viawireless communication or data transmission over one or more radiofrequency links or wireless communication channels sent from thecustomer device and/or wearable electronics device, an indication thatthe customer re-routed around or avoided one or more hazardous areas, avirtual log of routes that the customer has walked, jogged, or biked,and/or an indication of hazardous areas that the customer has traveledon foot (or biked through) and/or avoided; (6) generating or adjusting,via the one or more processors, a personal, health, life or otherinsurance premium or discount based upon (i) the customer re-routingaround the one or more hazardous areas, and/or (ii) the virtual logs ofpedestrian or bike routes taken; and/or (7) transmitting, via the one ormore processors and/or transceivers, and via wireless communication ordata transmission over one or more radio frequency links or wirelesscommunication channels, the adjusted insurance premium or discount tothe customer mobile device or wearable electronics device for customerreview to incentivize safer pedestrian or bicyclist travel. Theforegoing method may include additional, less, or alternate actions,including those discussed elsewhere herein, and may be implemented viacomputer systems comprising processors, transceivers, servers, and/orsensors.

High Risk Parking Lot Identification & Avoidance

FIG. 6 illustrates an exemplary computer-implemented method ofidentifying high risk parking lots and routing vehicles to alternateparking lots associated with lower risk of collision or theft 600. Themethod 600 may be implemented via one or more processors, transceivers,servers, and/or sensors, and/or via computer-executable instructionsstored on non-transitory computer-readable medium or media.

The method 600 may include (1) analyzing auto insurance claim data toidentify hazardous parking lots and/or classify the hazard 602. Forinstance historic or past data from actual auto insurance claims may beanalyzed, including images of damaged vehicles. The auto insurance claimdata may include a location of a vehicle collision that includes aparking lot. The hazards may be classified by vehicle collision, causeof collision, extent and type of vehicle damage, cost of vehicle repairor replacement, number of injured passengers, bicyclists, orpedestrians, type and extent of injuries, and/or vehicle theft.

The method 600 may include (2) building or generating a virtualnavigation (or road) map that depicts or virtually represents thehazardous parking lots 604. For instance, icons representing orindicating hazardous parking lots (such as circular icons) may besuperimposed upon an existing virtual navigation or road map.

The method 600 may include (3) transmitting the virtual navigation mapto autonomous vehicles, smart vehicles, vehicle navigation units, and/ormobile devices. The method 600 may include (4) determining if a current(GPS) location of the vehicle or mobile device (traveling within thevehicle) is within a predetermined distance of, or on route to, ahazardous parking lot 608. If so, the method 600 may include (5)generating an alert to notify the autonomous or smart vehicle or vehicleoperator 610. For instance, an alert may indicate that the parking lotis associated with a higher than normal risk of vehicle collision forvehicles entering or exiting the lot, and/or a higher than normal riskof vehicle theft.

The method 600 may include identifying a lower risk parking lot in thevicinity (such as within a few blocks or so) or along the route to theultimate destination (e.g., shopping mall, restaurant, park, ball park,hotel, etc.), and/or generating a route to the alternate or lower riskparking lot 612 for the autonomous vehicle or vehicle operator tofollow. The method 600 may include re-routing the autonomous vehicle tothe alternate parking lot 614. The method 600 may include updating anauto or other insurance premium or discount 616 based upon the insuredfollowing the recommendations provided associated with lower riskparking lots, and/or otherwise exhibiting risk averse behavior. Themethod 600 may include additional, less, or alternate functionality,including that discussed elsewhere herein.

In one aspect, a computer-implemented method of reducing vehiclecollisions may be provided. The method may include (1) analyzing, viaone or more processors, auto insurance claim data to identify hazardousareas (the hazardous areas being defined by, at least in part, GPSlocation or GPS coordinates); (2) building or generating, via the one ormore processors, a virtual navigation map of roads with the hazardousareas being visually depicted; (3) identifying, via the one or moreprocessors, public parking lots within the virtual navigation map, anddetermining whether each public parking lot is associated with ahazardous area or not; (4) determining, via the one or more processors,when a vehicle is approaching or parking in a public parking lotassociated with a hazardous area, and if so, then selecting, via the oneor more processors, a nearby public parking lot that is not associatedwith a hazardous area; (5) generating, via the one or more processors, aroute from a current position to the nearby public parking lot that isnot associated with a hazardous area; and/or (6) routing, via the one ormore processors and/or transceivers, the vehicle to the nearby publicparking lot using the route generated to facilitate reducing parking lotvehicle collisions and/or vehicle theft from parking lots. The methodmay include additional, less, or alternate actions, including thosediscussed elsewhere herein.

In another aspect, a computer system configured to reduce vehiclecollisions may be provided. The computer system may include one or moreprocessors, transceivers, and/or sensors configured to: (1) analyze autoinsurance claim data to identify hazardous areas (the hazardous areasbeing defined by, at least in part, GPS location or GPS coordinates);(2) build or generate a virtual navigation map of roads with thehazardous areas being visually depicted; (3) identify public parkinglots within the virtual navigation map, and determine whether eachpublic parking lot is associated with a hazardous area or not; (4)determine when a vehicle is approaching or parking in a public parkinglot associated with a hazardous area, and if so, then selecting, via theone or more processors, a nearby public parking lot (such as within apredetermined distance, for instance, 1 or 2 miles) that is notassociated with a hazardous area; (5) generate a route from a currentposition to the nearby public parking lot that is not associated with ahazardous area; and/or (6) route or re-route the vehicle to the nearbypublic parking lot not associated with a hazardous area using the routegenerated to facilitate reducing parking lot vehicle collisions and/orvehicle theft from parking lots. The computer system may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

In another aspect, a computer-implemented method of reducing vehiclecollisions may be provided. The method may include (1) analyzing, viaone or more processors, auto insurance claim data to identify hazardousareas (the hazardous areas being defined by GPS location or GPScoordinates); (2) building or generating, via the one or moreprocessors, a virtual navigation map of roads with the hazardous areasbeing visually depicted; (3) identifying, via the one or moreprocessors, public parking lots within the virtual navigation map, anddetermine whether each public parking lot is associated with a hazardousarea or not; and/or (4) determining, via the one or more processors,when a vehicle is approaching or parking in a public parking lotassociated with a hazardous area, and if so, then generating a haptic,visual, or audio alert to alert the driver to facilitate reduced vehiclecollisions and theft.

The method may also include determining, via the one or more processors,when a vehicle is approaching or parking in a public parking lotassociated with a hazardous area, and if so, then selecting, via the oneor more processors, a nearby public parking lot that is not associatedwith a hazardous area; generating, via the one or more processors, aroute from a current position to the nearby public parking lot that isnot associated with a hazardous area; and/or routing, via the one ormore processors and/or transceivers, the vehicle to the nearby publicparking lot using the route generated to facilitate reducing parking lotvehicle collisions and/or vehicle theft from parking lots. The methodmay include additional, less, or alternate actions, including thosediscussed elsewhere herein.

Additional Considerations

In one aspect, a customer may opt-in to a customer program. The customermay give their affirmative consent or permission to have certain typesof data collected and analyzed, such as mobile device, smart vehicle,telematics data, sensor data, smart home data, auto insurance claimdata, and/or vehicle collision data. In return, a remote server maygenerate alerts associated with hazardous areas from analysis of thecustomer data. The remote server may also update virtual navigation mapswith hazardous areas being depicted. The remote server may generateinsurance discounts to reward risk averse behavior. The remote servermay perform other functionality as well, including that discussedelsewhere herein.

All of the foregoing methods discussed herein may be include additional,less, or alternate actions, including those discussed elsewhere herein.All of the foregoing methods may be implemented via one or more local orremote processors, transceivers, servers, and/or sensors, and/or viacomputer-executable instructions stored on computer-readable medium ormedia. The foregoing computer systems may also include additional, less,or alternate functionality, including that discussed elsewhere herein.

Although this detailed description contemplates various embodiments, itshould be understood that the legal scope of any claimed system ormethod is defined by the words of the claims set forth at the end ofthis patent. This detailed description is to be construed as exemplaryonly and does not describe every possible embodiment, as describingevery possible embodiment would be impractical, if not impossible.

It is therefore intended that the foregoing detailed description beregarded as illustrative rather than limiting, and that it be understoodthat it is the following claims, including all equivalents, that areintended to define the spirit and scope of this invention.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently in certain embodiments.

As used herein, any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This description, and theclaims that follow, should be read to include one or at least one. Thesingular also includes the plural unless it is obvious that it is meantotherwise.

In various embodiments, hardware systems described herein may beimplemented mechanically or electronically. For example, a hardwaresystem may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC) to perform certain operations). A hardware system mayalso comprise programmable logic or circuitry (e.g., as encompassedwithin a general-purpose processor or other programmable processor) thatis temporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware systemmechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

References to a “memory” or “memory device” refer to a device includingcomputer-readable media (“CRM”). “CRM” refers to a medium or mediaaccessible by the relevant computing system for placing, keeping, and/orretrieving information (e.g., data, computer-readable instructions,program modules, applications, routines, etc.). Note, “CRM” refers tomedia that is non-transitory in nature, and does not refer todisembodied transitory signals, such as radio waves. The CRM of any ofthe disclosed memory devices may include volatile and/or nonvolatilemedia, and removable and/or non-removable media. The CRM may include,but is not limited to, RAM, ROM, EEPROM, flash memory, or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium which may be used tostore information and which may be accessed by the computing system. Oneor more of the disclosed memory devices may be coupled a processor via amemory interface. A memory interface is circuitry that manages the flowof data between the memory device and the bus of the computer system towhich it is coupled.

References to a “communication interface” or “network interface”generally refer to one or more interfaces for a system that enable thesystem to send information/data to other system, and/or to receiveinformation/data from other systems or devices. These other systems ordevices may include input devices (e.g., keyboard, mouse, etc.), outputdevices (e.g., a display device, speakers, etc.), networking equipment(e.g., routers, modems, etc.), and other computing devices (e.g.,servers, mobile devices, etc.). In some instances, the communicationinterface of a system may be utilized to establish a direct connectionto another system. In some instances, a communication interface of asystem enables the system to connect to a network (via a link).Depending on the embodiment, the communication interface may includecircuitry for permitting wireless communication (e.g., short-rangeand/or long-range communication) or wired communication with one or moredevices or systems using any suitable communications protocol. Forexample, the communication interface 204 shown in FIG. 2 may supportWi-Fi (e.g., an 802.11 protocol), Ethernet, Bluetooth, high frequencysystems (e.g., 900 MHZ, 2.4 GHZ, and 5.6 GHZ communication systems),infrared, transmission control protocol/internet protocol (“TCP/1P”)(e.g., any of the protocols used in each of the TCP/IP layers),hypertext transfer protocol (“HTTP”), BitTorrent, file transfer protocol(“FTP”), real-time transport protocol (“RTP”), real-time streamingprotocol (“RTSP”), secure shell protocol (“SSH”), any othercommunications protocol, or any combination thereof. The communicationinterface 204 may include circuitry that enables the system to beelectrically or optically coupled to another device (e.g., via a coaxcable or fiber optic cable) and to communicate with that other device.

A “communication link” or “link” is a pathway or medium connecting twoor more nodes. A link may be a physical link and/or a logical link. Aphysical link is the interface and/or medium(s) over which informationis transferred, and may be wired or wireless in nature. Examples ofphysicals links may include a cable with a conductor for transmission ofelectrical energy, a fiber optic connection for transmission of light,and/or a wireless electromagnetic signal that carries information viachanges made to one or more properties of an electromagnetic wave(s).

A logical link between two or more nodes represents an abstraction ofthe underlying physical links and/or intermediary nodes connecting thetwo or more nodes. For example, two or more nodes may be logicallycoupled via a logical link. The logical link may be established via anycombination of physical links and intermediary nodes (e.g., routers,switches, or other networking equipment).

A link is sometimes referred to as a “communication channel.” In awireless communication system, the term “communication channel” (or just“channel”) generally refers to a particular frequency or frequency band.A carrier signal (or carrier wave) may be transmitted at the particularfrequency or within the particular frequency band of the channel. Insome instances, multiple signals may be transmitted over a singleband/channel. For example, signals may sometimes be simultaneouslytransmitted over a single band/channel via different sub-bands orsub-channels. As another example, signals may sometimes be transmittedvia the same band by allocating time slots over which respectivetransmitters and receivers use the band in question.

The performance of certain of the operations may be distributed amongone or more processors, not only residing within a single machine, butdeployed across a number of machines. In some example embodiments, thedescribed processor or processors may be located in a single location(e.g., within a home environment, an office environment or as a serverfarm), while in other embodiments the processors may be distributedacross a number of locations.

Words such as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

Finally, unless a claim element is defined by reciting the word “means”and a function without the recital of any structure, it is not intendedthat the scope of any claim element be interpreted based upon theapplication of 35 U.S.C. § 112(f).

The invention claimed is:
 1. A computer-implemented method, the methodcomprising: identifying, via the one or more processors, public parkinglots within a virtual navigation map, and determining whether eachpublic parking lot is associated with a hazardous area or not;determining, via the one or more processors, that a vehicle isapproaching or parking in a public parking lot associated with ahazardous area along, or near to, a preexisting navigation route to anultimate destination; in response to determining that the vehicle isapproaching or parking in the public parking lot associated with ahazardous area, automatically selecting, via the one or more processors,a nearby public parking lot that is along, or within a predeterminedvicinity of, a preexisting navigation route, wherein the nearby publicparking lot is not associated with a hazardous area; generating, via theone or more processors, a route from a current position of the vehicleto the nearby public parking lot that is not associated with a hazardousarea; and routing, via one or more transceivers and/or the one or moreprocessors, the vehicle to the nearby public parking lot using thegenerated route.
 2. The computer-implemented method of claim 1, furthercomprising generating, via the one or more processors, an audio alertand/or an haptic alert when the vehicle is approaching or parking in thepublic parking lot associated with a hazardous area.
 3. Thecomputer-implemented method of claim 1, further comprising generating,via the one or more processors, a visual alert when the vehicle isapproaching or parking in the public parking lot associated with ahazardous area.
 4. The computer-implemented method of claim 1, whereinthe hazardous area is an area associated with a higher risk of vehiclecollision and/or theft compared to a normal level of risk.
 5. Thecomputer-implemented method of claim 4, wherein the nearby publicparking lot that is not associated with a hazardous area is a publicparking lot associated with a lower risk of vehicle collision.
 6. Thecomputer-implemented method of claim 4, wherein the nearby publicparking lot that is not associated with a hazardous area is a publicparking lot associated with a lower risk of theft.
 7. Thecomputer-implemented method of claim 1, wherein the nearby publicparking lot is within a predetermined distance of the public parking lotassociated with a hazardous area.
 8. The computer-implemented method ofclaim 1, further comprising determining, via the one or more processors,one or more risk averse behaviors performed by an operator of thevehicle during operation of the vehicle, and updating, via the one ormore processors, an insurance premium or discount based upon the one ormore risk averse behaviors.
 9. The computer-implemented method of claim8, wherein the one or more risk averse behaviors include a following ofthe generated route to the nearby public parking lot that is notassociated with a hazardous area.
 10. The computer-implemented method ofclaim 1, further comprising: analyzing, via one or more processors, autoinsurance claim data to identify hazardous areas, the hazardous areasbeing defined, at least in part, by GPS location or GPS coordinates; andbuilding or generating, via the one or more processors, a virtualnavigation map of roads within the hazardous areas.
 11. A computersystem comprising: one or more processors; and one or more memoriesstoring computer-executable instructions that, when executed, cause theprocessor to: identify public parking lots within a virtual navigationmap, and determine whether each public parking lot is associated with ahazardous area or not; determine that a vehicle is approaching orparking in a public parking lot associated with a hazardous area along,or near to, a preexisting navigation route to an ultimate destination;in response to determining that the vehicle is approaching or parking inthe public parking lot associated with a hazardous area, select a nearbypublic parking lot that is along, or within a predetermined vicinity of,the preexisting navigation route, wherein the nearby public parking lotis not associated with a hazardous area; generate a route from a currentposition of the vehicle to the nearby public parking lot that is notassociated with a hazardous area; and route or reroute the vehicle tothe nearby public parking lot using the generated route.
 12. Thecomputer system of claim 11, wherein the processor routes or reroutesthe vehicle further using one or more transceivers.
 13. The computersystem of claim 11, wherein the computer-executable instructions, whenexecuted on the processor, further cause the processor to generate anaudio alert and/or a haptic alert when the vehicle is approaching orparking in the public parking lot associated with a hazardous area. 14.The computer system of claim 11, wherein the computer-executableinstructions, when executed on the processor, further cause theprocessor to generate visual alert when the vehicle is approaching orparking in the public parking lot associated with a hazardous area. 15.The computer system of claim 11, wherein the hazardous area is an areaassociated with a higher risk of vehicle collision and/or theft comparedto a normal level of risk.
 16. The computer system of claim 15, whereinthe nearby public parking lot that is not associated with a hazardousarea is a public parking lot associated wi a lower risk of vehiclecollision.
 17. The computer system of claim 15, wherein the nearbypublic parking lot that is not associated with a hazardous area is apublic parking lot associated with a lower risk of theft.
 18. Thecomputer system of claim 11, wherein the nearby public parking lot iswithin a predetermined distance of the public parking lot associatedwith a hazardous area.
 19. The computer system of claim 11, wherein thecomputer-executable instructions, when executed on the processor,further cause the processor to: determine one or more risk aversebehaviors performed by an operator of the vehicle during operation ofthe vehicle, and update an insurance premium or discount based upon theone or more risk averse behaviors.
 20. The computer system of claim 19,wherein the one or more risk averse behaviors include a following of thegenerated route to the nearby public parking lot that is not associatedwith a hazardous area.